Climate Change Research

Climate Change Research

Drought monitoring based on the Standardized Precipitation-Evaporation Index SPEI under the influence of climate change and the XGBoost algorithm

Document Type : Original Article

Authors
Hadi Ramezsani Etedali 1
Mojgan Ahmadi 2
1 Associate Professor, Department of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran
2 Ph.D. Graduate, Department of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran
10.30488/ccr.2026.568744.1321
Abstract
In recent years, following the occurrence of global warming and changes in climate patterns and meteorological parameters, the frequency of droughts has increased in many regions of the world. In this study, drought monitoring using the SPEI index and examining the characteristics of this phenomenon (intensity, magnitude, duration) under climate change conditions at the Qazvin synoptic station in the historical period 2014-1986 and the future periods 2050-2026, 2051-2075 and 2100-2076 under SSP2-4.5 and SSP5-8.5 scenarios at time scales of 3, 6, 9 and 12 months have been studied. To reduce the uncertainty associated with individual models and enhance the reliability of the estimates, a machine learning–based Multi-Model Ensemble (MME) approach was employed. The XGBoost algorithm was used to perform a nonlinear and optimized combination of the outputs from three CMIP6 climate models: MIROC6, ACCESS-CM2, and CNRM-CM6-1. Drought characteristics were subsequently calculated based on the ensemble dataset derived from the combined outputs of these three climate models. In the SSP2-4.5 scenario, drought changes showed a slight increasing trend compared to the past. In the period 2026–2050, the average intensity was observed between 2.03 and 3.02, and the magnitude between 0.91 and 1.25. In the pessimistic scenario SSP5-8.5, the trend of increasing drought intensity and magnitude was more obvious. In the first half of the century (2026–2050), the intensity varied between 1.91 and 3.70, and the magnitude between 1.1 and 1.14. Overall, the results showed that both the intensity and magnitude indices of drought have an increasing trend from the past to the future, but the increase in intensity is more dramatic. Thus, it can be concluded that in the future, the study area will face an increase in the frequency, severity, and persistence of droughts, which further highlights the need for adaptation planning, water resource management, and the development of strategies to reduce the effects of drought
Keywords
Atmospheric general circulation model
ensemble model
machine learning
Scenarios Six Report

Subjects

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